Signal processing apparatus and method

ABSTRACT

A method and an apparatus to analyze two measured signals that are modeled as containing desired and undesired portions such as noise, FM and AM modulation. Coefficients relate the two signals according to a model defined. The method and apparatus are particularly advantageous to blood oximetry and pulserate measurements.

PRIORITY CLAIM

This application is a continuation of U.S. application Ser. No.11/842,128, filed on Aug. 20, 2007, which is a continuation of U.S.application Ser. No. 10/791,683, filed on Mar. 2, 2004, which is acontinuation of U.S. application Ser. No. 09/547,588, filed Apr. 11,2000 (now U.S. Pat. No. 6,699,194), which is a continuation of U.S.application Ser. No. 09/081,539, filed May 19, 1998 (now U.S. Pat. No.6,067,462), which is a divisional of U.S. application Ser. No.08/834,194, filed Apr. 14, 1997 (now U.S. Pat. No. 6,002,952). Thepresent application incorporates the foregoing disclosures herein byreference.

FIELD OF THE INVENTION

The present invention relates to the field of signal processing. Morespecifically, the present invention relates to the processing ofmeasured signals, containing a primary signal portion and a secondarysignal portion, for the removal or derivation of either the primary orsecondary signal portion when little is known about either of thesecomponents. The present invention is especially useful for physiologicalmonitoring systems including blood oxygen saturation systems andpulserate measurement systems. The present invention further relates toa method and apparatus for signal processing of signals in order tocompute an estimate for pulserate.

BACKGROUND

Signal processors are typically employed to remove or derive either theprimary or secondary signal portion from a composite measured signalincluding a primary signal portion and a secondary signal portion. Forexample, a composite signal may contain a primary signal portioncomprising desirable data and a secondary signal portion comprisingnoise. If the secondary signal portion occupies a different frequencyspectrum than the primary signal portion, then conventional filteringtechniques such as low pass, band pass, and high pass filtering areavailable to remove or derive either the primary or the secondary signalportion from the total signal. Fixed single or multiple notch filterscould also be employed if at least one of the primary and secondarysignal portions exists at a fixed frequency band.

It is often the case that an overlap in frequency spectrum between theprimary and secondary signal portions exists. Complicating mattersfurther, the statistical properties of one or both of the primary andsecondary signal portions may change with time. In such cases,conventional filtering techniques are ineffective in extracting eitherthe primary or secondary signal. If, however, a description of eitherthe primary or secondary signal portion can be derived, correlationcanceling, such as adaptive noise canceling, can be employed to removeeither the primary or secondary signal portion of the signal isolatingthe other portion. In other words, given sufficient information aboutone of the signal portions, that signal portion can be extracted.

Conventional correlation cancelers, such as adaptive noise cancelers,dynamically change their transfer function to adapt to and removeportions of a composite signal. However, correlation cancelers andadaptive noise cancelers require either a secondary reference or aprimary reference which correlates to either the secondary signalportion only or the primary signal portion only. For instance, for ameasured signal containing noise and desirable signal, the noise can beremoved with a correlation canceler if a noise reference is available.This is often the case. Although the amplitudes of the reference signalsare not necessarily the same as the amplitudes of the correspondingprimary or secondary signal portions, the reference signals have afrequency spectrum which is similar to that of the primary or secondarysignal portions.

In many cases, nothing or very little is known about the secondary andprimary signal portions. One area where measured signals comprising aprimary signal portion and a secondary signal portion about which noinformation can easily be determined is physiological monitoring.Physiological monitoring generally involves measured signals derivedfrom a physiological system, such as the human body. Measurements whichare typically taken with physiological monitoring systems includeelectrocardiographs, blood pressure, blood gas saturation (such asoxygen saturation), capnographs, other blood constituent monitoring,heart rate, respiration rate, electro-encephalograph (EEG) and depth ofanesthesia, for example. Other types of measurements include those whichmeasure the pressure and quantity of a substance within the body such ascardiac output, venous oxygen saturation, arterial oxygen saturation,bilirubin, total hemoglobin, breathalyzer testing, drug testing,cholesterol testing, glucose testing, and carbon dioxide testing,protein testing, carbon monoxide testing, and other in-vivomeasurements, for example. Complications arising in these measurementsare often due to motion of the patient, both external and internal(muscle movement, vessel movement, and probe movement, for example),during the measurement process.

Many types of physiological measurements can be made by using the knownproperties of energy attenuation as a selected form of energy passesthrough a test medium such as a finger, shown schematically in FIG. 1.

A blood gas monitor is one example of a physiological monitoring systemwhich is based upon the measurement of energy attenuated by biologicaltissues or substances. Blood gas monitors transmit light into the testmedium and measure the attenuation of the light as a function of time.The output signal of a blood gas monitor which is sensitive to thearterial blood flow contains a component having a waveformrepresentative of the patient's arterial pulse. This type of signal,which contains a component related to the patient's pulse, is called aplethysmographic wave, and is shown in FIG. 2A as a curve s(t) 201.Plethysmographic waveforms are used in blood gas saturationmeasurements. As the heart beats, the amount of blood in the arteriesincreases and decreases, causing increases and decreases in energyattenuation, illustrated by a cyclic wave seen in the curve 201.

Typically, a digit such as a finger, an ear lobe, or other portion ofthe body where blood flows close to the skin, is employed as the mediumthrough which light energy is transmitted for blood gas attenuationmeasurements. The finger comprises skin, fat, bone, muscle, etc., asshown FIG. 1, each of which attenuates energy incident on the finger ina generally predictable and constant manner. However, when fleshyportions of the finger are compressed erratically, for example by motionof the finger, energy attenuation becomes erratic.

An example of a more realistic measured waveform is shown in FIG. 2B, asa curve M(t) 202. The curve 202 illustrates the effect of motion andnoise n(t) added to the clean waveform s(t) shown in FIG. 201. Theprimary plethysmographic waveform portion of the signal M(t) is thewaveform representative of the pulse, corresponding to the sawtooth-likepattern wave in curve 201. The large, secondary motion-inducedexcursions in signal amplitude obscure the primary plethysmographicsignal s(t). Even small variations in amplitude make it difficult todistinguish the primary signal component s(t) in the presence of asecondary signal component n(t).

A pulse oximeter is a type of blood gas monitor which non-invasivelymeasures the arterial saturation of oxyten in the blood. The pumping ofthe heart forces freshly oxygenated blood into the arteries causinggreater energy attenuation. As well understood in the art, the arterialsaturation of oxygenated blood may be determined from the depth of thevalleys relative to the peaks of two plethysmographic waveforms measuredat separate wavelengths. Patient movement introduces motion artifacts tothe composite signal as illustrated in the plethysmographic waveformillustrated in FIG. 2B. These motion artifacts distort the measuredsignal.

SUMMARY OF THE INVENTION

The present invention involves several different embodiments using thenovel signal model in accordance with the present invention to estimatethe desired signal portion of a measured data signal where the measureddata contains desired and undesired components. In one embodiment, asignal processor acquires a first measured signal and a second measuredsignal. The first signal comprises a desired signal portion and anundesired signal portion. The second measured signal comprises a desiredsignal portion and an undesired signal portion. The signals may beacquired by propagating energy through a medium and measuring anattenuated signal after transmission or reflection. Alternatively, thesignals may be acquired by measuring energy generated by the medium.

In one embodiment, the desired signal portions of the first and secondmeasured signals are approximately equal to one another, to with a firstconstant multiplier. The undesired signal portions of the first andsecond measured signals are also approximately equal to one another, towithin a second constant multiplier. A scrubber coefficient may bedetermined, such that an estimate for the first signal can be generatedby inputting the first and second measured signals, and the scrubbercoefficient into a waveform scrubber. The output of the waveformscrubber is generated by multiplying the first measured signal by thescrubber coefficient and then adding the result to the second measuredsignal.

In one embodiment, the scrubber coefficient is determined by normalizingthe first and second measured signals, and then transforming thenormalized signals into a spectral domain. The spectral domain signalsare then divided by one another to produce a series of spectral ratiolines. The need for waveform scrubbing can be determined by comparingthe largest ratio line to the smallest ratio line. If the differencedoes not exceed a threshold value, the no scrubbing is needed. If thedifference does exceed a threshold value, then the waveform must bescrubbed, and the scrubbing coefficient corresponds to the magnitude ofthe largest ratio line.

Another aspect of the present invention involves a physiological monitorhaving a signal processor which computes an estimate for an unknownpulserate from the measured data. In one embodiment, the signalprocessor receives measured data from a detector that measures aphysiological property related to the heartbeat. The signal processortransforms the data into a spectral domain and then identifies a seriesof spectral peaks and the frequencies associated with those peaks. Thesignal processor then applies a set of rules to the spectral peaks andthe associated frequencies in order to compute an estimate for thepulserate.

In yet another embodiment of the pulserate detector, the signalprocessor performs a first transform to transform the measured data intoa first transform space. The signal processor then performs a secondtransform to transform the data from the first transform space into asecond transform space. The signal processor then searches the data inthe second transform space to find the pulserate.

In another embodiment, the transform into the first transform space is aspectral transform such as a Fourier transform. In another embodiment,the transform into the second transform space is a spectral transformsuch as a Fourier transform. In yet another embodiment, once the datahas been transformed into the second transform space, the signalprocessor performs a 1/x mapping on the spectral coordinates beforesearching for the pulserate.

In another embodiment, the signal processor transforms the measured datainto a first spectral domain, and then transforms the data from thefirst spectral domain into a second spectral domain. After twicetransforming the data, the signal processor performs a 1/x remapping onthe coordinates of the second spectral domain. The signal processor thensearches the remapped data for the largest spectral peak correspondingto a pulserate less than 120 beats per minute. If such a peak is found,then the signal processor outputs the frequency corresponding to thatpeak as being the pulserate. Otherwise, the signal processor searchesthe data transformed into the first spectral domain for the largestspectral peak in that domain, and outputs a pulserate corresponding tothe frequency of the largest peak in the first spectral domain.

In another embodiment of the pulserate detector, the signal processorfirst transforms the measured data into a first spectral domain. Thenthe signal processor takes the magnitude of the transformed data andthen transforms the magnitudes into a second spectral domain. Then thesignal processor then performs a 1/x mapping of the spectralcoordinates. After the 1/x mapping, the signal processor feeds thetransformed and remapped data into a neural network. The output of theneural network is the pulserate.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates a typical finger.

FIG. 2A illustrates an ideal plethysmographic waveform.

FIG. 2B illustrates a plethysmographic waveform which includes a motioninduced erratic signal portion.

FIG. 3 illustrates a schematic diagram of a physiological monitor inaccordance with the teachings of one aspect of the present invention

FIG. 4 illustrates an example of a low noise emitter current driver withaccompanying digital to analog converter in accordance with theteachings of one aspect of the present invention.

FIG. 5 illustrates the absorption properties of hemoglobin at variouswavelength.

FIG. 6 illustrates one cycle of an idealized plethysmographic waveformfor various levels of oxygen saturation at a fixed perfusion.

FIG. 7 illustrates a block diagram of the signal processing used tocompute the ratio of red signal to infrared signal in accordance withone aspect of the present invention.

FIG. 8 is a graph which illustrates the relationship between thered/infrared ratio and blood oxygen saturation.

FIG. 9 is a graph which illustrates the relationship between the idealred and infrared signals, and the relationship between measured red andinfrared signals.

FIG. 10 illustrates a model for measured data in a pulse oximeter.

FIG. 11 is an idealized frequency domain plot of the red and infraredtransmission signals

FIG. 12 is a block diagram of a motion detector and removal system inaccordance with one aspect of the present invention.

FIG. 13 is a flowchart showing the processing steps of a motion detectorand removal method in accordance with one aspect of the presentinvention.

FIG. 14A is an idealized frequency domain plot of an plethysmographicwave.

FIG. 14B is an idealized frequency domain plot of a plethysmographicwave showing the effect of FM modulation.

FIG. 14C is an idealized time domain plot of a superimposed pair ofplethysmographic waves that can be used to model an FM modulatedplethysmographic wave.

FIG. 15 is an idealized frequency domain plot of a plethysmographic waveshowing the effects of AM modulation.

FIG. 16 is a group of idealized frequency domain plots that illustratethe various categories used in the rule based method for determiningpulserate in accordance with one aspect of the present invention.

FIG. 17 is a block diagram which illustrates the signal processing usedto determine pulse rate by the pleth to pulserate transform method(PPRT) in accordance with one aspect of the present invention.

FIG. 18 is a flowchart showing the process steps of the rule basedpulserate detection method.

FIG. 19 illustrates a schematic diagram of a physiological monitor thatuses a neural network in accordance with the teachings of one aspect ofthe present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention involves a system which uses first and secondmeasured signals that each contain a primary signal portion and asecondary signal portion. In other words, given first and secondcomposite signals c₁(t)=s₁(t)+n₁(t) and c₂(t)=s₂(t)+n₂(t), the system ofthe present invention can be used to isolate either the primary signalportion s(t) or the secondary signal portion n(t) of the two compositesignals. Following processing, the output of the system provides a goodapproximation n″(t) to the secondary signal portion n(t) or a goodapproximation s″(t) to the primary signal portion s(t).

The system of the present invention is particularly useful where theprimary signal portion s(t), the secondary signal portion n(t), or both,may contain one or more of a constant portion, a predictable portion, anerratic portion, a random portion, etc. The primary signal approximations″(t) or the secondary signal approximation n″(t) is derived by removingas many of the secondary signal portions n(t) or primary signal portionss(t) from the composite signal c(t) as possible. The remaining signalforms either the primary signal approximation s″(t) or the secondarysignal approximation n″(t), respectively. The constant portion and thepredictable portion of the secondary signal n(t) are easily removed withtraditional filtering techniques, such as simple subtraction, low pass,band pass, and high pass filtering. The erratic portion is moredifficult to remove due to its unpredictable nature. If something isknown about the erratic signal, even statistically, it could be removed,at least partially, from the measured signal via traditional filteringtechniques. However, often no information is known about the erraticportion of the secondary signal n(t). In this case, traditionalfiltering techniques are usually insufficient.

In order to remove the secondary signal n(t), a signal model inaccordance with the present invention is defined as follows for thefirst and second measured signals c₁ and c₂:

$\begin{matrix}{c_{1} = {s_{1} + n_{1}}} & (1) \\{c_{2} = {s_{2} + n_{2}}} & (2) \\{{{with}\mspace{14mu} s_{1}} = {{r_{a}s_{2}\mspace{14mu} {and}\mspace{14mu} n_{1}} = {r_{v}n_{2}}}} & (3) \\{{{or}\mspace{14mu} r_{a}} = {{\frac{s_{1}}{s_{2}}\mspace{14mu} {and}\mspace{14mu} r_{v}} = \frac{n_{1}}{n_{2\;}}}} & (4)\end{matrix}$

where s₁ and n₁ are at least somewhat (preferably substantially)uncorrelated and s₂ and n₂ are at least somewhat (preferablysubstantially) uncorrelated. The first and second measured signals c₁and c₂ are related by correlation coefficients r_(a) and r_(v) asdefined above. The use and selection of these coefficients is describedin further detail below.

In accordance with one aspect of the present invention this signal modelis used in combination with a waveform scrubber to remove the undesiredportion of the measured signals.

The description that follows can best be understood in view of thefollowing list which briefly describes how the invention is broken downand described according to the following topics:

1. A general overview of pulse oximetry measurements, in connection withFIGS. 1 through 4, provides a general theory and system block diagramfor a red/infrared pulse oximetry apparatus for measurement ofphysiological data such as blood oxygen saturation and pulserate;

2. A more detailed description of the relationship between the dataRD(t) measured using red light, and the data IR(t) measured usinginfrared light, normalization of RD(t) and IR(t), and the relationshipof the normalized RD(t) and IR(t) to blood oxygen saturation, isprovided in connection with FIGS. 5 through 8;

3. A mathematical model and description of the effect of motionartifacts on RD(t) and IR(t) and a method for detecting and removing theartifacts to create a clean spectrum F(ω)=RD(ω)/IR(ω), are provided inconnection with FIGS. 10 through 13;

4. A mathematical model and a description of a rule based signalprocessing technique used by the pulse oximeter to determine pulserate,are provided in connection with FIGS. 14 through 16; and

5. A mathematical model and a description of a transform based signalprocessing technique used by the pulse oximeter to determine pulserate,are provided in connection with FIG. 17.

Pulse Oximetry Measurements

A specific example of a physiological monitor using a processor of thepresent invention to determine a secondary reference n′(t) for input toa canceler that removes erratic motion-induced secondary signal portionsis a pulse oximeter. Pulse oximetry may also be performed using aprocessor of the present invention to determine a primary signalreference s′(t) which may be used for display purposes or for input to aprocessor to derive information about patient movement, pulserate, andvenous blood oxygen saturation.

A pulse oximeter typically causes energy to propagate through a mediumwhere blood flows close to the surface, for example, an ear lobe, or adigit such as a finger, a forehead or a fetus' scalp. An attenuatedsignal is measured after propagation through or reflected from themedium. The pulse oximeter estimates the saturation of oxygenated blood.

Freshly oxygenated blood is pumped at high pressure from the heart intothe arteries for use by the body. The volume of blood in the arteriesand arterioles varies with the heartbeat, giving rise to a variation inabsorption of energy at the rate of the heartbeat, or the pulse. Theblood scatters both red and infrared light, and thus as the volume ofblood changes, the amount of scattering changes as well. Typically theeffects due to scattering are small when compared to the effects due tothe change in blood volume.

Oxygen depleted, or deoxygenated, blood is returned to the heart by theveins along with unused oxygenated blood. The volume of blood in theveins varies with back pressure due to breathing as well as localuncontrolled motion of muscles. These variations typically occur at arate that is much slower than the heartbeat. Thus, when there is nomotion induced variation in the thickness of the veins, venous bloodcauses a low frequency variation in absorption of energy. When there ismotion induced variation in the thickness of the veins, the scatteringchanges as well and this absorption is coupled with the erraticvariation in absorption due to motion artifacts.

In absorption measurements using the transmission of energy through amedium, two light emitting diodes (LEDs) are positioned close to aportion of the body where blood flows close to the surface, such as afinger, and a photodetector is positioned near the LEDs. Typically, inpulse oximetry measurements, one LED emits a visible wavelength,preferably red, and the other LED emits an infrared wavelength. However,one skilled in the art will realize that other wavelength combinations,as well as combinations of more than two wavelengths, could be used. Thefinger comprises skin, tissue, muscle, both arterial blood and venousblood, fat, etc., each of which absorbs light energy differently due todifferent absorption coefficients, different concentrations, differentthicknesses, and changing optical pathlengths. When the patient is notmoving, absorption is substantially constant except for the flow ofblood. The constant attenuation can be determined and subtracted fromthe signal via traditional filtering techniques. When the patient moves,this causes perturbation such as changing optical pathlength due tomovement of background fluids (e.g., venous blood having a differentsaturation than the arterial blood). Therefore, the measured signalbecomes erratic. Erratic motion induced noise typically cannot bepredetermined and/or subtracted from the measured signal via traditionalfiltering techniques. Thus, determining the oxygen saturation ofarterial blood and venous blood becomes more difficult.

FIG. 3 depicts a general hardware block diagram of a pulse oximeter 299.A sensor 300 has two light emitters 301 and 302, such as LED's. One LED301 emitting light of red wavelengths and another LED 302 emitting lightof infrared wavelengths are placed adjacent a finger 310. Aphotodetector 320, which produces an electrical signal corresponding tothe attenuated visible and infrared light energy signals is, locatednear the LED's 301 and 302. The photodetector 320 is connected to frontend analog signal conditioning circuity 330.

The front end analog signal conditioning circuit 330 has outputs coupledto an analog to digital conversion circuit 332. The analog to digitalconversion circuit 332 has outputs coupled to a digital signalprocessing system 334. The digital signal processing system 334 providesthe desired parameters as outputs for a display 336. Outputs for displayare, for example, blood oxygen saturation, heart rate, and a cleanplethysmographic waveform.

The signal processing system also provides an emitter current controloutput 337 to a digital-to-analog converter circuit 338 which providescontrol information for a set of light emitter drivers 340. The lightemitter drivers 340 couple to the light emitters 301, 302. The digitalsignal processing system 334 also provides a gain control output 343 forthe front end analog signal conditioning circuitry 330.

FIG. 4 illustrates a preferred embodiment for the combination of theemitter drivers 340 and the digital to analog conversion circuit 338. Asdepicted in FIG. 4, the driver comprises first and second input latches321, 322, a synchronizing latch 323, a voltage reference 324, a digitalto analog conversion circuit 325, first and second switch banks 326,327, first and second voltage to current converters 328, 329 and the LEDemitters 301, 302 corresponding to the LED emitters 301, 302 of FIG. 3.

The preferred driver depicted in FIG. 4 is advantageous in that thepresent inventors recognized that much of the noise in the oximeter 299of FIG. 3 is caused by the LED emitters 301, 302. Therefore, the emitterdriver circuit of FIG. 4 is designed to minimize the noise from theemitters 301, 302. The first and second input latches 321, 322 areconnected directly to the digital signal processor (DSP) bus 337.Therefore, these action of these latches significantly minimizes thebandwidth (resulting in noise) present on the DSP bus 337 which passesthrough to the driver circuitry of FIG. 4. The outputs of the first andsecond input latches 321, 322, only change when the latches detect theirrespective address on the DSP bus 337. The first input latch 321,receives the setting for the digital to analog converter circuit 325.The second input latch 322 receives switching control data for theswitch banks 326, 327. The synchronizing latch 323 accepts thesynchronizing pulses which maintain synchronization between theactivation of emitters 301, 302 and the analog to digital conversioncircuit 332.

The voltage reference 324 is also chosen as a low noise DC voltagereference for the digital to analog conversion circuit 325. In addition,in the present embodiment, the voltage reference 324 has a lowpassoutput filter with a very low corner frequency (e.g., 1 Hz in thepresent embodiment). The digital to analog converter 325 also has alowpass filter at its output with a very low corner frequency (e.g., 1Hz). The digital to analog converter 338 provides signals for each ofthe emitters 301, 302.

In the present embodiment, the output of the voltage to currentconverters 328, 329 are switched such that with the emitters 301, 302connected in back-to-back configuration, only one emitter is active anany given time. In addition, the voltage to current converter 328 or 329for the inactive emitter is switched off at its input as well, such thatit is completely deactivated. This reduces noise from the switching andvoltage to current conversion circuitry. In the present embodiment, lownoise voltage to current converters are selected (e.g., Op 27 Op Amps),and the feedback loop is configured to have a low pass filter to reducenoise. In the present embodiment, the low pass filtering function of thevoltage to current converters 328, 329 has a corner frequency of justabove 316.7 Hz, which is the switching speed for the emitters, asfurther discussed below. Accordingly, the preferred driver circuit ofFIG. 4, minimizes the noise of the emitters 301, 302.

In general, each of the red and infrared light emitters 301, 302 emitsenergy which is partially absorbed by the finger 310 and the remainingenergy is received by the photodetector 320. The photodetector 320produces an electrical signal which corresponds to the intensity of thelight energy striking the photodetector 320. The front end analog signalconditioning circuitry 330 receives the intensity signals and filtersand conditions these signals, as further described below, for furtherprocessing. The resultant signals are provided to the analog-to-digitalconversion circuitry 332 which converts the analog signals to digitalsignals for further processing by the digital signal processing system334. In the present embodiment, the output of the digital signalprocessing system 334 provides clean plethysmographic waveforms of thedetected signals and provides values for oxygen saturation and pulserate to the display 336.

It should be understood that in different embodiments of the presentinvention, one or more of the outputs may be provided. The digitalsignal processing system 334 also provides control for driving the lightemitters 301, 302 with an emitter current control signal on the emittercurrent control output 337. This value is a digital value which isconverted by the digital-to-analog conversion circuit 338 which providesa control signal to the emitter current drivers 340. The emitter currentdrivers 340 provide the appropriate current drives for the red emitter301 and the infrared emitter 302. Further detail of the operation of thephysiological monitor for pulse oximetry is explained below.

In the present embodiment, the light emitters 301, 302 are driven viathe emitter current driver 340 to provide light transmission withdigital modulation at 316.7 Hz. In the present embodiment, the lightemitters 301, 302 are driven at a power level which provides anacceptable intensity for detection by the detector and for conditioningby the front end analog signal conditioning circuitry 330. Once thisenergy level is determined for a given patient by the digital signalprocessing system 334, the current level for the red and infraredemitters is maintained constant. It should be understood, however, thatthe current may be adjusted for changes in the ambient room light andother changes which would effect the voltage input to the front endanalog signal conditioning circuitry 330. In the present invention, thered and infrared light emitters 301, 302 are modulated as follows: forone complete 316.7 Hz red cycle, the red emitter 301 is activated forthe first quarter cycle, and off for the remaining three-quarters cycle;for one complete 316.7 Hz infrared cycle, the infrared light emitter 302is activated for one quarter cycle and is off for the remaining threequarters cycle. In order to only receive one signal at a time, theemitters are cycled on and off alternatively, in sequence, with eachonly active for a quarter cycle per 316.7 Hz cycle and with a quartercycle separating the active times.

The light signal is attenuated (amplitude modulated) by the pumping ofblood through the finger 310 (or other sample medium). The attenuated(amplitude modulated) signal is detected by the photodetector 320 at the316.7 Hz carrier frequency for the red and infrared light. Because onlya single photodetector is used, the photodetector 320 receives both thered and infrared signals to form a time division multiplexed (TDM)signal. The TDM signal is provided to the front analog signalconditioning circuitry 330 and may be demodulated by either before orafter analog to digital conversion.

Saturation Curves and Normalization

The ability of the apparatus 299 to measure the desired physiologicproperties lies in the optical absorption properties of hemoglobin, asillustrated in FIG. 5. FIG. 5 shows an x axis 501 corresponding to awavelength of light and a y axis 502 corresponding to an absorptioncoefficient for light passing through a medium. A reduced hemoglobin(Hb) curve 503 shows the absorption properties of oxygen poorhemoglobin. An oxygen rich hemoglobin (Hb02) curve 504 shows theabsorption properties of oxygen rich hemoglobin. A reference line 506highlights the region where the curves 503 and 504 pass through a valueon the x axis 501 corresponding to 660 nm (nanometer) wavelength (thenominal operational wavelength of the red emitter 301). A reference line505 highlights the region where the curves 503 and 504 pass through avalue on the x axis 501 corresponding to 905 nm wavelength (the nominaloperational wavelength of the infrared emitter 302).

At the reference line 506, the Hb curve 503 shows more absorption thanthe HbO2 curve 504. Conversely, at the reference line 505, the HbO2curve shows more absorption than the Hb curve 503. The pulse oximetercan thus measure the blood oxygen saturation by measuring absorption ofthe blood at 660 nm and 905 nn, and the comparing the two absorptionmeasurements.

According to the Beer-Lambert law of absorption, the intensity of lighttransmitted through an absorbing medium is given by:

I=I ₀ e ^(−εdc)   (5)

where I₀ is the intensity of the incident light, ε is the absorptioncoefficient, c is the concentration coefficient and d is the thicknessof the absorbing medium. In pulse oximetry applications, there are twosources, red and infrared, and thus two incident intensities, I_(0,RD)for red, and I_(0,IR) for infrared. Furthermore, in blood there are twoconcentrations of interest, namely the concentration of oxygen poorhemoglobin, denoted by C_(Hb) and the concentration of oxygen richhemoglobin, denoted by C_(HbO2). The combination of the two opticalwavelengths and the two concentrations means that there are fourabsorption coefficients, namely ε_(RD),_(Hb,) ε_(RD),_(Hb02), ε_(IR,Hb),and ε_(IR),_(Hb02.) Using these quantities, and assuming no timevariation in any of the values except d, gives two separate Beer-Lambertequations for the pulse oximeter.

I _(RD) =I _(0,RD) _(e) ^(−[ε) ^(RD,Hb) ^(c) ^(Hb) ^(+ε) ^(RD,HbO2) ^(c)^(HbO2) ^(]d(t))   (6)

I _(IR) =I _(0,IR) _(e) ^(−[ε) ^(IR,Hb) ^(c) ^(Hb) ^(+ε) ^(IR,HbO2) ^(c)^(HbO2) ^(]d(t))   (7)

The measurement apparatus 299 does not provide a capability formeasuring the incident terms and I_(0,RD) and I_(0,IR) appearing in theabove equation, and thus, strictly speaking, the value of I_(RD) andI_(IR) cannot be determined. However, in the pulse oximeter, onlydifferential measurements are necessary. In other words, it is only thetime varying nature of the values I_(RD) and I_(IR) and the relationshipbetween the values that are important. The time variation in d(t) occursprimarily because blood flows in and out of the finger with eachheartbeat. As blood flows into the finger, the effective value of d, aswell as the scattering component, increases, and as blood flows out, theeffective value of d and the scattering decreases. There are also timevariations in the concentrations C_(Hb) and C_(HbO2) as the blood oxygensaturation level changes. Fortunately, these variations are slowcompared to the variations in d(t), and they can be ignored.

FIG. 6 illustrates one cycle of an idealized plethysmographic waveformfor various levels of oxygen saturation. The figure shows an x-y plothaving a time axis 601 in the x direction, and a transmission axis 602in the y direction. The transmission axis 602 shows the intensity of thered light transmitted through the finger. A curve 604 shows thetransmission of red light for 80% blood oxygen saturation. A curve 603shows transmission of red light for 98% blood oxygen saturation. Thecurves 603 and 604 are intended to show different values of saturationgiven the same perfusion d. As shown in the figure, at the beginning ofa heartbeat, red transmission is at a maximum because the fingercontains relatively little blood. As the heartbeat progresses, blood isperfused into the finger and the amount of light transmissiondiminishes. Transmission diminishes because the additional material, theblood, increases the effective path length d in Equation (6).Transmission also diminishes somewhat because of scattering produced bythe blood. If the blood is highly saturated with oxygen, as shown in thecurve 603, the transmission diminishes only slightly because, as shownin FIG. 5, HbO2 has a relatively small absorption coefficient in the redwavelengths. If the blood has low oxygen saturation, as shown in thecurve 604, then transmission diminishes significantly more because, asshown in FIG. 5, Hb has a relatively large absorption in the redwavelengths.

If FIG. 6 were redrawn to show the transmission properties of infraredlight, then the curves 603 and 604 would essentially be interchanged,because as shown in FIG. 5, more infrared light is absorbed by HbO2 thanis absorbed by Hb.

The above properties of the absorption of light by Hb and HbO2advantageously provide a way to measure blood oxygen saturation bycomputing the ratio of red light to infrared light. FIG. 7 shows oneembodiment of a signal processing apparatus for obtaining the desiredratio. FIG. 7 shows a red signal path which begins at a RD signal input701. The RD signal input 701 corresponds to the amount of red lighttransmitted through the finger. The signal at the RD signal input 701 isfed into a logarithmic amplifier 702 which in turn feeds a bandpassfilter 703. The output of the bandpass filter 703 is fed into aroot-means-square (RMS) detector 704. The output of the RMS detector 704is fed to a numerator input of a divider 709. FIG. 7 further shows an IRsignal path comprising an IR input 705, a logarithmic amplifier 706, abandpass filter 707, and an RMS detector 708. The output of the RMSdetector 708 is fed to a denominator input of the divider 709.

In a preferred embodiment, the elements shown in FIG. 7 are part of thesignal processing block 334 shown in FIG. 3. The RD input 701 and IRinput 705 are obtained by demultiplexing the output of the detector 320,also shown in FIG. 3. The signals at the inputs 701 and 705 correspondto I_(RD) and I_(IR) respectively, and are similar to the curves shownin FIG. 6. However, in the preferred embodiment, the signals areuncalibrated (i.e., the scale of the y axis 602 is unknown) because thevalue of I_(0,RD) and I_(0,IR) in Equations (6) and (7) are unknown.This is not an impediment to the measurement of the blood oxygensaturation, because saturation can be obtained without reference toeither I_(0,RD) or I_(0,IR) as follows. Taking the natural logarithm (insignal processing blocks 702 and 706) of both Equation (6) and Equation(7) yields:

ln(I _(RD))=ln(I _(0,RD))−[ε_(RD,Hb) C _(Hb)+ε_(RD,HbO2) C _(HbO2) ]d(t)  (8)

ln(I _(IR))=ln(I _(0,IR))−[ε_(IR,Hb) C _(Hb)+ε_(IR,HbO2) C _(HbO2) ]d(t)  (9)

Applying a bandpass filter (in signal processing blocks 703 and 707)removes the non-time varying components, and allows Equations (8) and(9) to be rewritten as:

RD(t)=−[ε_(RD,Hb) C _(Hb)+ε_(RD,HbO2) C _(HbO2) ]d(t)   (10)

IR(t)=−[ε_(IR,Hb) C _(Hb)+ε_(IR,HbO2) C _(HbO2) ]d(t)   (11)

FIG. 9 shows a plot of RD(t) versus IR(t). In FIG. 9, an x axis 810corresponds to IR(t) and a y axis 811 corresponds to RD(t). A straightline 812, having a positive slope, illustrates how the plot of RD(t)versus IR(t) would appear under ideal conditions of no noise, noscattering, and no motion artifacts. A curve 813 depicts a morerealistic locus of points RD(t) versus IR(t) under normal measurementconditions. FIG. 8 shows a plot of blood oxygen saturation versus theratio of the RMS value of RD(t)/IR(t). FIG. 8 shows an x axis 801corresponding to blood oxygen saturation from 0% to 100% and a y axiscorresponding to RMS(RD(t))/RMS(IR(t)) ranging from 0 to 3. A saturationcurve 803 depicts the relationship between RMS(RD(t))/RMS(IR(t)) andblood oxygen saturation. The blood oxygen saturation is given bysat=100*C_(HbO2)/(C_(Hb)+C_(HbO2)). It is obtained by dividing Equation(10) by Equation (11) and solving for C_(HbO2) and C_(Hb) using themeasured values of RD(t) and IR(t), and the known values of theabsorption coefficients. Note that the unknown quantity d(t) isapproximately the same for both red and infrared and thus divides out.

Detection and Removal of Motion Artifacts

Persons skilled in the art know that the data obtained during pulseoximetry measurements using red and infrared light are oftencontaminated due to motion. Identification and removal of these motionartifacts is often a prerequisite to any signal processing used toobtain blood oxygen saturation, pulserate, or other physiological data.FIG. 10 schematically illustrates an additive noise process model thatcan be used, in conjunction with Equation (10) and Equation (11) toapproximate the measured data contaminated by such motion artifacts.FIG. 9 shows a desired signal input s(t) 901 and an undesired signalinput n(t) 902. The desired signal s(t) 901 and the undesired signalinput n(t) 902 are summed by a summing junction 903. The output of thesumming junction 903 represents the actual measured data M(t) 904. Asapplied to Equation (10), the desired signal s(t) 901 corresponds toRD(t). As applied to Equation (11), the desired signal s(t) 901represents IR(t).

In FIG. 10, the desired signal s(t) 901, which contains the desiredphysiologic data is not directly accessible. Only the measured signalM(t) 904 is accessible. Thus, the problem is to obtain an estimate ofthe undesired signal n(t) 902 so that it can be subtracted from themeasured signal to yield the desired signal. One such method forremoving the undesired signal n(t) involves the use of a correlationcanceler as is found in U.S. Pat. No. 5,432,036 (the '036 patent)assigned to the same assignee as the present application.

The correlation canceler is a complex operation requiring significantcomputational overhead. In accordance with one embodiment of the presentinvention, a new and novel method for detecting the presence of motionartifacts and removing these artifacts can be found in the spectraldomain representations of the signals RD(t) and IR(t). Use of thespectral domain representations is more compatible with many of thedigital signal processor (DSP) devices currently available. Further, theuse of the spectral domain representations provides a method, asdisclosed below, a way to estimate the amount of motion and noiseseparately. As a further advantage, it is noted that, under certaincircumstances, the correlation canceler would drive the output signal tozero. The spectral domain method of detecting artifacts is far lesslikely to drive the output signal to zero.

FIG. 11 shows an idealized illustration of the spectrum of RD(t) andIR(t). FIG. 11 shows an x axis 1101 corresponding to frequency, and a yaxis 1102 corresponding to the magnitude of the spectral components. Thespectrum of RD(t), denoted mathematically as:

RD(ω)=

[RD(t)]  (12)

is shown as a series of peaks, comprising a first spectral peak 1104 ata fundamental frequency f₀, a second spectral peak 1107 at a firstharmonic f₁ and a third spectral peak 1110 at a frequency f_(m). Thespectrum of IR(t), denoted mathematically as:

IR(ω)=

[IR(t)]  (13)

is shown as a series of peaks, comprising a first spectral peak 1103 atthe fundamental frequency f₀, a second spectral peak 1106 at the firstharmonic f₁ and a third spectral peak 1109 at a frequency f_(m). Theratio of the spectral components, given by RD(ω)/IR(ω), is shown as afirst ratio line 1105 at the fundamental frequency f₀, a second ratioline 1108 at the first harmonic f₁ and a third ratio line 1111 at thefrequency f_(m). As discussed below, when there are no motion artifactsin the spectrum of FIG. 11, all of the spectral peaks will occur atharmonic frequencies, and all of the ratio lines will have approximatelythe same height. Under conditions of no motion, difference in the heightof the ratio lines will be due primarily to scattering effects. Thespectral peaks 1110 and 1109 corresponding to the frequency f_(m), whichis not necessarily a harmonic of f₀, represent peaks due to motion, andtherefor having an amplitude different from that of the first spectralline 1105 and the second spectral line 1108.

One skilled in the art will recognize that the representations in FIG.11 have been idealized for the purposes of explanation. In particular,in actual measured data, especially data contaminated by noise and otherundesired components, the frequencies of the spectral peaks of RD(ω) donot correspond exactly to the spectral peaks of IR(ω). Althoughcorresponding frequencies will typically be quite close, variations of afew percent are not unexpected. Thus, for example, it will be obvious toone skilled in the art that, due to the imperfections in most measureddata, the fundamental frequency f₀ found for RD(ω) will often bedifferent from the fundamental frequency f₀ found for IR(ω). The samecomments would apply to other harmonics (e.g., f₁ and f₂) as well. Inone embodiment of the present invention, the frequencies f₀, f₁, f₂ (orequivalently ω₀, ω₁, ω₂), etc. (hereinafter the frequency peaks)correspond to the frequency peaks found in RD(ω), and the ratiosRD(ω)/IR(ω) are calculated using values of RD(ω) and IR(ω) at thosefrequencies, regardless of whether they also happen to correspond to afrequency peak in IR(ω). In another embodiment of the present invention,the frequency peaks correspond the frequency peaks found in IR(ω), andthe ratios RD(ω)/IR(ω) are calculated using the values of RD(ω) andIR(ω) at those frequencies, regardless of whether they also happen tocorrespond to a frequency peak in RD(ω). In yet another embodiment ofthe present invention, the frequency peaks of RD(ω) and IR(ω) are foundseparately, and the ratios RD(ω)/IR(ω) are calculated by matching thefrequency peaks of RD(ω) with the nearest frequency peaks of IR(ω).

In an ideal measurement, the red and infrared spectra are the same towithin a constant scale factor. Thus, in an ideal measurement, all ofthe ratio lines 1105, 1108 and 1111 have substantially the sameamplitude. Any differences in the amplitude of these lines is likely dueto motion or other contaminations represented by n(t) (includingscattering effects). For each component, red and infrared, the model ofFIG. 9 can be expressed as:

$\begin{matrix}{{{S_{1}(t)} = {{A(t)} + {N(t)}}}{{S_{2}(t)} = {{{{{rA}(t)}{h(t)}} + {\mu \; N\; {\eta (t)}}} \approx {{{rA}(t)} + {\mu \; {N(t)}}}}}} & (14)\end{matrix}$

where S₁(t) represents the infrared signal, A(t) represents the desiredinfrared signal and N(t) represents the noise signal. Likewise, S₂(t)represents the measured red signal, r represents the ratio of red toinfrared (RD(ω)/IR(ω)) expected in an uncontaminated measurement, and μrepresents the ratio of red noise to infrared noise. The quantities h(t)and η(t) are primarily due to scattering, and thus required because,strictly speaking, A(t) and N(t) in the red channel and infraredchannels are not simply related by a constant. However, for mostpurposes, the quantities h(t) and η(t) are sufficiently close to unitythat they can be ignored.

Introducing an arbitrary scaling factor a into the equation for S₁, andthen subtracting the two equations yield (for notational convenience,the time dependence of S, A and N will not be explicitly shown):

αS ₁ −S ₂ =A(α−r)+N(α−μ)   (15)

Two special cases arise from Equation (17). First, when α=r, Equation(17) reduces to:

$\begin{matrix}{N = \frac{{\alpha \; S_{1}} - S_{2}}{r - \mu}} & (16)\end{matrix}$

Second, when α=μ, Equation (17) reduces to:

$\begin{matrix}{A = \frac{{\alpha \; S_{1}} - S_{2}}{\mu - r}} & (17)\end{matrix}$

The values of μ and r can be found from the ratio of RD(ω)/IR(ω) asshown in FIG. 11 and the following two observations. First, since r isthe coupling coefficient between red and infrared (the ratio of red toinfrared) then r is expected to be reasonably constant over shortperiods of time. Likewise, μ is expected to be relatively constantbecause it is merely the coupling coefficient between the noise in thered and infrared signals. Second, the condition μ=r is not expected tooccur because that would mean that the saturation due to arterial bloodis equal in magnitude to the saturation due to venous blood. One skilledin the art will recognize, that, except for short periods of time,arterial blood saturation and venous blood saturation cannot be thesame, because a living body consumes oxygen from the blood as the bloodpasses from the arteries to the veins. Arterial blood and venous bloodsaturation can be the same for short periods of time, and even reversed,especially where blood pooling has occurred and a quick desaturation istaking place. It is always expected that μ is larger than r. Therefore,in one embodiment of the present invention, the value of μ correspondsto the largest peak in FIG. 11 and the value of r corresponds to thesmallest peak of FIG. 11. Further, the presence of motion artifacts inthe data are easily detectable by examination of the relationshipbetween μ and r.

In a preferred embodiment, the value of μ is found by classifying theratio peaks according to a ratio threshold g. The ratio threshold g iscomputed identifying the first N ratio lines R_(N) associated with thefirst N spectral peaks. The ratio threshold g is then computed as amodified center of mass for the R_(N) lines according to the followingequation.

$g = \frac{N}{\sum\limits_{i = 0}^{N - 1}\frac{1}{R_{i}}}$

Each ratio line is then compared with the ratio threshold g. Only thoseratio lines whose magnitude is larger than the ratio threshold g areincluded in a set Y of ratio lines. Only ratio lines in the set Y areused in the calculation of μ. In one embodiment, the value of μ is themagnitude of the largest ratio peak in the set of ratio peaks R_(i) fori=0 . . . N. In an alternate embodiment, the value of μ is the magnitudeof the ratio peak corresponding to the largest spectral peak in the setY.

The values of μ and r are used to determine whether motion artifacts arepresent. In one embodiment, the ratio μ/r is calculated. If the ratio isclose to unity, then, to within a constant scaling factor, the spectrumRD(ω) is approximately the same as the spectrum IR(ω) and thus there areno motion artifacts. If, on the other hand, the ratio μ/r is not closeto unity, then the shape of the spectrum RD(ω) is different from thespectrum IR(ω), signaling the presence of motion artifacts, and thus thespectrum must be scrubbed according to Equation (17).

In a preferred embodiment, a delta is computed by subtracting themagnitude of the smallest ratio line from the magnitude of the largestratio line. If the delta is smaller than a threshold value, then thespectrum RD(ω) is approximately the same as the spectrum IR(ω) and thusthere are no motion artifacts, but only variations due to scattering.If, on the other hand, the delta μ−r is greater than the thresholdvalue, then the shape of the spectrum RD(ω) is different from thespectrum IR(ω), signaling the presence of motion artifacts, and thus thespectrum must be scrubbed according to Equation (19).

FIG. 12 shows a block diagram of a signal processing system thatimplements the motion detection and spectrum scrubbing operations inaccordance with one aspect of the present invention. In FIG. 12, aninput from a single sensor 1202 that receives red and infrared light isfed into a demultiplexer 1204 which separates the red and infraredsignals. The red signal is fed into a filter 1206 which removes unwantedspectral components. The output of the filter 1206 is normalized (as isdescribed in the text describing FIG. 7) by the series combination of alog amplifier 1208, and a bandpass filter 1210. The output RD(t) of thebandpass filter 1210 is fed into a Fourier transform block 1214. Theoutput of the transform block 1214 is fed into the numerator term of adivider 1230. The infrared output from the demultiplexer 1204 isprocessed, in the same fashion as the red signal, by the seriescombination of a filter 1220, a log amplifier 1222, a bandpass filter1224, and a Fourier transform block 1228. The output of the Fouriertransform block 1228 is fed into a denominator input of the divider1230. An output of the divider 1230 is fed into a process block 1240which determines μ, and r, and which computes α according to theflowchart of FIG. 13. An a output of the process block 1240 is fed as aninput to a time domain waveform scrubber 1242. The time domain waveformscrubber 1242 has three input terminals, A, B, and D, and a singleoutput terminal C. The time domain scrubber terminal A is connected tothe output of the bandpass filter 1210. The time domain scrubberterminal B is connected to the output of the bandpass filter 1224. Thetime domain scrubber terminal D is connected to the a output of theprocess block 1240. Inside the time domain scrubber 1242, the terminal Ais connected to a signal input of a gain controlled amplifier 1244. Again control input of the amplifier 1244 is connected to the scrubberterminal D. The scrubber terminal B is connected to a plus input of anadder 1246. An output of the amplifier 1244 is connected to a minusinput of the adder 1246. An output of the adder 1246 is connected to aFourier transform block 1248. An output of the Fourier transform block1248 is connected to the scrubber output terminal C.

One skilled in the art will recognize that the linearity of the Fouriertransform allows the scrubbing operation to be carried out in thefrequency domain as well. A frequency domain scrubber 1240 is also shownin FIG. 12. The frequency domain scrubber 1260 has the same fourterminals, A, B, C, and D, as the time domain scrubber 1242.

Inside the frequency domain scrubber 1260, the terminal A is connectedto a signal input of a Fourier transform block 1262. The output of theFourier transform block 1262 is connected to a signal input of a gaincontrolled amplifier 1266. A gain control input of the amplifier 1266 isconnected to the scrubber terminal D. The scrubber terminal B isconnected to a Fourier transform block 1264. An output of the transformblock 1264 is connected to a plus input of an adder 1268. An output ofthe amplifier 1266 is connected to a minus input of the adder 1268. Anoutput of the adder 1268 is connected to the scrubber output terminal C.

Regardless of whether the time domain scrubber 1242 or the frequencydomain scrubber 1260 is used, the scrubber output C is aplethysmographic waveform in the frequency domain at a terminal 1249.Ideally, the waveform at terminal 1249 is cleaner (e.g., has a bettersignal to noise ratio) than the waveform at either scrubber input A orscrubber input B. The waveform at terminal 1249 can be displayed on adisplay (not shown) or sent to a rule based pulserate detector 1250and/or a transform based pulserate detector 1252.

FIG. 13 is a flowchart which illustrates the process steps performed bythe signal processing block 1240 in FIG. 12. The flowchart of FIG. 13begins at a start block 1302 and proceeds to a process block 1304. Inthe process block 1304, the spectrum F(ω)=RD(ω)/IR(ω) is searched forthe largest ratio line μ and smallest ratio line r and the frequenciesf_(μ) and f_(r) at which those two lines occur. The process thenadvances to a process block 1306 where the difference, delta d=μ−r iscomputed. The process then proceeds to a decision block 1308. If, in thedecision block 1308, the delta d is greater than a threshold value, thenmotion artifacts are present and the process advances to a decisionblock 1312 to continue the calculation of α. Otherwise, if in theprocess block 1308, the delta d is less than the threshold value, thenno scrubbing ins necessary and the process advances to a process block1310. Since both μ and r are ratios, they are dimensionless. The delta dis also dimensionless. In a preferred embodiment, the threshold value is0.5. In the process block 1310, the value of α is set to 0, whichessentially disables the scrubber. In the decision block 1312, thefrequencies f_(μ) and f_(r) are compared. If the two frequencies areclose together, then the process advances to a process block 1314;otherwise, the process advances to a process block 1316. In the processblock 1314 the value of α is set to α=(μ+r)/2. In the process block 1316the value of α is set to a=μ. The process blocks 1310, 1314 and 1316 alladvance to a process block 1318 where the value of α is sent to thescrubber. Upon completion of the process block 1318, the process jumpsback to the process block 1304 to recalculate α.

One skilled in the art will recognize that the flowchart in FIG. 13 canbe modified to perform additional functions. For example, upon detectingthat motion artifacts are present (during the transition to the decisionblock 1312), an indicator can be lit, or an alarm can be sent, to warnthe medical clinician that motion artifacts were present. In yet anotherembodiment, upon transitioning to the process block 1312, the delta dcould be examined against a second threshold to determine whether themotion artifacts were so severe that further processing was impossible.

Rule Based Pulserate Detection

In addition to measuring blood oxygen saturation, a pulse oximeter isable to perform continuous monitoring of a patient's pulserate. As shownin FIG. 6, each heartbeat forces blood into the arteries and thatincrease in blood is detected by the plethysmographic apparatus. Thus,the scrubbed spectrum present at the terminal 1250 in FIG. 12 containssome of the information that would be found in the Fourier spectrum ofan electrocardiograph (EKG).

FIG. 14A shows an ideal spectrum F(ω) of a clean plethysmographic wavefrom a heart that is beating with a very regular beat. The figure showsan x axis 1410 corresponding to frequency and a y axis 1411corresponding to the magnitude of the spectral components. A curve 1412shows |F(ω)|. It is well known, that the waveform of a human heartbeatis not a pure sine wave, and thus the curve 1412 is not a singlespectral line, but rather a first spectral line at a fundamentalfrequency f₀ and a series of decreasing harmonics at 2f₀, 3f₀, etc.Clearly, under these conditions, the frequency f₀ corresponds to thepulserate.

Often the ideal waveform of FIG. 14A is not seen because the heart isbeating irregularly or because the cardio-vascular system of the subjectis producing a large dicrotic notch. This leads to a spectrum in whichthe largest spectral line is not necessarily the pulserate. FIG. 14Bshows one example of such a waveform. Like FIG. 14A, FIG. 14B shows an xaxis 1420 corresponding to frequency, and a y axis 1421 corresponding toamplitude. A curve 1422 shows F(ω). However, unlike the curve 1412, thecurve 1422 shows a spectral line at a fundamental frequency f₀, and aseries of harmonics f₁ and f₂ having amplitudes larger than theamplitude of the fundamental, with f₂ being the largest. The curve 1422illustrates the folly of attempting to determining pulserate merely byfinding the largest spectral line. Such an algorithm, applied to thecurve 1422 would report a pulserate that was three times higher than theactual pulse rate.

The spectrum shown in curve 1422 is commonly seen in plethysmographicwaveforms and corresponds to a frequency modulated (FM) heartbeat. Inaccordance with one aspect of the present invention, a rule based methodfor determining the pulserate of a heart producing the spectrum of FIG.14B is disclosed. The rule based method is based on a time domain model(a “stick model”) plotted in FIG. 14C. This elegantly simple modelcaptures the essential feature of the plethysmographic waveform. FIG.14C shows an x axis 1401 corresponding to time, and a y axis 1402corresponding to the amount of blood being forced into the arteries by aheart. FIG. 14C further shows two overlapping waveforms. A firstwaveform 1403 shows to blood being forced into the arteries during afirst time interval T₁. A second waveform 1404 shows blood being forcedinto the arteries during a second time interval T₂. The two timeintervals, T₁ and T₂, do not overlap and the total period of the sum ofthe two waveforms is T₁+T₂. The sum of the two waveforms represents aheart that is beating at two different pulserates on alternate beats.For example, if the heartbeats were numbered, then on every evennumbered beat, the heartbeat would last T₁ seconds. On every oddnumbered beat, the heartbeat would last T₂ seconds. This is not artunusual occurrence, and there are physiological reasons why this occurs.The spectrum shown in FIG. 14B is essentially the spectrum of thesuperposition of the waveforms 1403 and 1404.

Amplitude modulation (AM) of the plethysmographic waveform is alsopossible and common. Amplitude modulation occurs primarily when theheart beats with different strength on different heartbeats. FIG. 15shows a sample spectrum F(ω) that exhibits the effects of AM. FIG. 15shows a frequency axis 1501 and a spectrum axis 1502. The spectrumconsists of a series of spectral peaks 1503 and sidebands 1504. Oneskilled in the art will recognize this as a typical AM spectrum of acarrier and its associated modulation sidebands. Under some conditions,of high pulserate and substantial modulation bandwidth, the sidebands1504 due to one spectral peak 1503 can overlap the sidebands due to anadjacent spectral peak. This overlap significantly complicates thewaveform (not shown).

In accordance with one aspect of the present invention, the pulseratecan be determined in the presence of FM and AM distortions byclassifying the spectrum as one of five categories grouped into threecases. The five categories are illustrated as idealized graphs in: FIG.16A, illustrating Case I; FIG. 1613, illustrating Case II; and FIG. 16C,illustrating Case III.

FIG. 16A shows a plot 1600 having an x axis 1601 corresponding tofrequency and a y axis 1602 corresponding to the magnitude of thespectrum. FIG. 16A also shows a first spectral line 1603, a secondspectral line 1604 and a third spectral line 1605. The three spectrallines 1603, 1604, and 1605 show a monotonically decreasing amplitudewhere the decrease is approximately linear.

FIG. 16B shows a first plot 1610 having an x axis 1611 corresponding tofrequency and a y axis 1612 corresponding to the magnitude of thespectrum. The first plot 1610 also shows a first spectral line 1613, asecond spectral line 1614 and a third spectral line 1615. The thirdspectral line 1615 has the smallest amplitude of the three lines. Thesecond spectral line 1614 has the largest amplitude of the three lines,and its amplitude rises significantly above a line drawn from the firstspectral line 1613 to the third spectral line 1615.

FIG. 16B also shows a second plot 1620 having an x axis 1621corresponding to frequency and a y axis 1622 corresponding to themagnitude of the spectrum. The second plot 1620 also shows a firstspectral line 1623, a second spectral line 1624 and a third spectralline 1625. The first spectral line 1623 has the smallest amplitude ofthe three lines. The second spectral line 1624 has the largest amplitudeof the three lines, and its amplitude rises significantly above a linedrawn from the first spectral line 1623 to the third spectral line 1625.

FIG. 16C shows a first plot 1630 having an x axis 1631 corresponding tofrequency and a y axis 1632 corresponding to the magnitude of thespectrum. The first plot 1630 also shows a first spectral line 1633, asecond spectral line 1634 and a third spectral line 1635. The amplitudesof the three spectral lines are monotonically increasing, and theincrease is approximately linear.

FIG. 16C also shows a second plot 1640 having an x axis 1641corresponding to frequency and a y axis 1642 corresponding to themagnitude of the spectrum. The second plot 1640 also shows a firstspectral line 1643, a second spectral line 1644 and a third spectralline 1645. The third spectral line 1645 has the smallest amplitude ofthe three lines. The second spectral line 1644 has the largest amplitudeof the three lines, and its amplitude is significantly below a linedrawn from the first spectral line 1643 to the second spectral line1645.

In accordance with one aspect of the present invention, the pulserate isdetermined by identifying the largest three spectral lines, thenmatching the spectrum to one of the idealized spectra shown by the plots1600, 1610, 1620, 1630, or 1640, and then applying one of a set of rulesto determine the pulserate. It will be understood by one skilled in theart that, although the frequencies of the spectral shown in the plots1600, 1610, 1620, 1630, or 1640 appear to be harmonically related. Inpractice the spectral lines may not correspond to frequencies which areharmonics.

The details of the rule based process are shown in the flowchart of FIG.18. FIG. 18 begins at a start block 1802 and proceeds to aninitialization process block 1804. In the block 1804, the values of thepulserate, p, and confidence factor, s, are set to zero. When theprocess reaches an exit block, p will contain the pulserate (or zero ifno pulserate was found), and σ will contain a confidence factorindicating related to the pulserate (or zero if no pulserate was found).After completing the initialization block 1804, the process advances toa search process block 1805 where the spectrum |F(ω)| is searched forthe first three spectral peaks. After finding the peaks, the processadvances to a decision block 1806 where the process checks the number ofspectral peaks actually found. If, in the decision block 1806, thenumber of peaks is less than three, then the process advances to adecision block 1808; otherwise, the process jumps forward to a processblock 1812. If, in the process block 1808, the number of peaks isgreater than zero, then the process advances to a process block 1810;otherwise, the process jumps to an exit block. In the process block1810, the value of p is set to the frequency corresponding to thelargest of the spectral peaks, the confidence value is set to 10, andthe process then advances to the exit block.

In the process block 1812, the first three spectral peaks are sorted bymagnitude, and the values assigned to variables A₀, A₁, and A₂ such thatA₀ is the magnitude of the largest peak, A₁ is the magnitude of themiddle peak, and A is the magnitude of the smallest peak. Also, in theprocess block 1812, variables f₀, f₁, and f₂, representing thefrequencies corresponding to A₀, A₁ and A₂ respectively, are set. Uponcompletion of the process block 1812, the process advances to a decisionblock 1814. In the decision block 1814, if A₀ is greater than or equalto 1.2*(A₁+A₂) and f₀ is less than 250, then the process advances to aprocess block 1816; otherwise the process jumps to a decision block1824. In the process block 1816, the value of p is set to p=f₀, and theprocess then advances to a decision block 1818. In the decision block1818, the values of f₀, f₁, and f₂ are checked to see if they areharmonics of one another. In a preferred embodiment, this is done bychecking to see whether a frequency f_(i) is within ten beats per minuteof being a integer multiple of another frequency f_(j) (where i,j=0, 1,or 2). If the decision block 1818 detects that the frequencies areharmonics, then the process advances to a process block 1820; otherwise,the process advances to a process block 1822. In the process block 1820,the value of σ is set to 60, and the process then advances to thedecision block 1824. In the process block 1822, the value of σ is set to50 and the process then advances to the decision block 1824.

In the decision block 1824, if A₀<1.2*(A₁+A₂), then the process advancesto a decision block 1826, otherwise the process advances to the exitblock. In the decision block 1826, if (f₀<f₁) and (f₀<f₂), then theprocess advances to a decision block 1828; otherwise the processadvances to a decision block 1938. In the decision block 1828, if thefrequencies f₀, f₁, and f₂ are harmonics, then the process advances to adecision block; otherwise, the process advances to a process block 1836.In the process block 1836, the value of p is set to p=f₀, the value of σis set to 90, and the process then advances to the decision block 1838.In the decision block 1830, if f₀ is less than 45 beats per minute, thenthe process advances to a process block 1834; otherwise, the processadvances to a process block 1832. In the process block 1832, the valueof p is set to p=f₀, the value of σ is set to σ=80, and the process thenadvances to the decision block 1838. In the process block 1834, thevalue of σ is set to p=(f₀+f₁+f₂)/3, the value of σ is set to σ=70, andthe process then advances to the decision block 1838.

In the decision block 1838, if f₀>f₁ or f₀>f₂, then the process advancesto a decision block 1840; otherwise, the process advances to a decisionblock 1846. In the decision block 1840, if (f₀>f_(i)) and (f₀, f₁ and f₂are harmonics) and (A₀<1.7A₁) and (30<f₁<130) then the process advancesto a decision block 1842; otherwise, the process advances to a processblock 1844. In the process block 1842, the value of p is set to p=f₁,the value of σ is set to σ=100, and the process then advances to thedecision block 1848. In the process block 1844, the value of p is set top=f₀, the value of σ is set to σ=110, and the process then advances tothe decision block 1848.

In the decision block 1848, if (f₀, f₁ and f₂, are harmonics) and f₀<100and (A₁+A₂)/A₀>1.5), then the process advances to a process block 1852;otherwise, the process advances to a process block 1850. In the processblock 1852, the value of p is set to p=(f₀+f₁+f₂)/3, the value of σ isset to σ=120, and the process then advances to the exit block. In theprocess block 1852, the value of p is set to p=f₀, the value of σ is setto σ=130, and the process then advances to the exit block.

As stated previously, when the process shown in FIG. 18 reaches the exitblock, p contains the pulserate, and σ contains the confidence factor.The confidence factor is a number indicating the likelihood that thevalue of p accurately represents the actual pulserate of the patient.

Transform Based Pulserate Detection

In accordance with another aspect of this invention, the pulserate canbe determined in the presence of FM and AM distortions by using a plethto pulserate transform (PPRT). FIG. 17 shows a schematic of a signalprocessing system that implements a PPRT. In FIG. 17, a time domainplethysmographic waveform f(t) is fed into an input 1701. The signal atthe input 1701 is fed into a Fourier transform block 1702 which forwardtransforms f(t) into the frequency domain. An output of the block 1702is expressed mathematically as F(ω)=

[f(t)]. The output of the block 1702 is fed into a magnitude block 1703which finds the magnitude of the signal F(ω). An output of the magnitudeblock 1703, shown as a plot 1713, is fed into a second forward Fouriertransform block 1704 which transforms the signal |F(ω)| into a signalG(x) where G(x)=

[|F(ω)|] and G(x) is a complex number. The output of the block 1704 isfed into a block 1705 which extracts the real portion of G(x). The realportion of G(x) is then fed into a 1/x mapping block 1706. An output ofthe mapping block 1706 is fed into a pulserate detector block 1707. Apulserate output from the detector block 1706 is sent to a display 1708.

In an alternate embodiment, the magnitude block could be replaced by ablock which extracts the real portion of the waveform. Likewise, theblock 1705 which extracts the real portion of G(x) could be replaced bya magnitude block which extracts |G(x)|.

One skilled in the art will recognize that the output of the magnitudeblock 1703 is merely the absolute value of the Fourier transform of theplethysmographic wave f(t) on a point by point basis. The graph 1713shows this signal as a series of spectral lines of varying amplitudes.In many cases, this spectrum will be similar to that shown in FIG. 1413,and has a fundamental frequency f₀, and a series of harmonics f₁ and f₂of various amplitudes. As shown in FIG. 14B, any attempt at determiningpulserate merely by finding the largest spectral line will lead toerroneous results. Further, the clean waveform of FIG. 14B, showing aseries of spectral peaks, will often be contaminated by AM sidebands asshown in FIG. 15. Thus the fundamental periodic nature of the heartbeatis not always readily apparent in the spectrum of plot 1713. This is thereason for the second Fourier transform in process block 1704.

The nature of the Fourier transform is to identify and quantify theperiodic nature of a function. If the waveform shown in the plot 1713were in the time domain, rather than the frequency domain, then theseries of pulses (the spectral lines of the plot 1713) would correspondto a periodic train of pulses, having a fundamental frequency given bythe pulse repetition frequency and modulated by the spectrum of theindividual pulses. Mathematically, it does not matter that the waveformof the plot 1713 is not in the time domain. The Fourier transform canstill be applied, and it will still produce a very strong spectral linecorresponding to the inherent periodicity, and corresponding componentstrength, of the waveform.

Thus, the operation of the block 1704, in performing a forward Fouriertransform on a frequency domain waveform is mathematically viable, andyields the desired data. The only unique ramification of the fact thatthe transformed data is already in the frequency domain rather than thetime domain is the effect on the x axis. It is well known to thoseskilled in the art, that the forward Fourier transform maps the x axisinto 1/x. This is most easily explained by noting that, normally, onewould transform f(t) into F(ω). Since t=1/ω (to within a constant factorof 2π) it is clear that a 1/x mapping has occurred. In the presentcontext, the 1/x mapping is undesirable because the data was already inthe frequency domain. Thus the mapping must be undone by the processblock 1706.

Once the waveform has been remapped in the process block 1707, it is asimple matter to find the desired pulserate in the process block 1707,because the pulserate will correspond to the largest spectral peak.Again, this occurs because the second Fourier transform “identifies” thedominant periodicity (e.g., the dominant string of harmonics) andcollapses that periodicity into a single spectral line. The pulseratedetector 1707 merely searches for the largest spectral peak and sends,to the display 1708, the frequency that corresponds to the largest peak.

In yet another embodiment, the process block 1707 looks for theexistence of a spectral peak below 120 beats per minute. If a spectralpeak below 120 beats per minute is found, then the frequencycorresponding that peak is the pulserate. Of, on the other hand, nospectral peak below 120 beats per minute is found, then the processblock 1707 finds the largest spectral peak in the original fourierspectrum that exists at the output of the Fourier transform block 1702.The pulserate is then the frequency corresponding to the largestspectral peak at the output of the Fourier transform block 1702.

In yet another embodiment, the ratio of the largest two peaks in thePPRT waveform 1716 can be used to generate a confidence factor thatprovides some indication of the accuracy of the computed pulserate. In apreferred embodiment, a contrast ratio is computed by dividing themagnitude of the largest peak in the PPRT waveform 1716 by the magnitudeof the second largest peak in the PPRT waveform 1716. A large contrastratio corresponds to high confidence that the computed pulserate isaccurate. A contrast ratio near unity corresponds to low confidence thatthe computed pulserate is accurate.

Neural Network Embodiments

In yet another embodiment, much of the signal processing can beaccomplished by a neural network. One skilled in the art will recognizethat the signal processing associated with the removal of motionartifacts involves non-linear and linear processes. The frequency domainwaveform scrubber 1260 and the time domain waveform scrubber 1242 areboth linear processes. However, the calculation of α in FIG. 12 is anon-linear process, in part because it includes the ratio operationrepresented by the process block 1230. The calculation of pulserate,either by the rule based method, or the PPRT method both involve ratiosand are thus non-linear processes as well.

One skilled in the art will appreciate that other non-linear filteringprocesses can be used. In particular, any of these non-linear processescan be performed by a neural network as shown in FIG. 19. FIG. 19depicts a general hardware block diagram of a pulse oximeter 299 thatemploys neural network processing. A sensor 300 has two light emitters301 and 302, such as LED's. One LED 301 emitting light of redwavelengths and another LED 302 emitting light of infrared wavelengthsare placed adjacent a finger 310. A photodetector 320, which produces anelectrical signal corresponding to the attenuated visible and infraredlight energy signals is, located near the LED's 301 and 302. Thephotodetector 320 is connected to front end analog signal conditioningcircuity 330.

The front end analog signal conditioning circuit 330 has outputs coupledto an analog to digital conversion circuit 332. The analog to digitalconversion circuit 332 has outputs coupled to a digital signalprocessing and neural network signal extraction system 1934. The signalprocessing system 1934 provides the desired parameters as outputs for adisplay 336. Outputs for display are, for example, blood oxygensaturation, heart rate, and a clean plethysmographic waveform.

The signal processing system also provides an emitter current controloutput 337 to a digital-to-analog converter circuit 338 which providescontrol information for a set of light emitter drivers 340. The lightemitter drivers 340 couple to the light emitters 301, 302. The signalprocessing system 1934 also provides a gain control output 343 for thefront end analog signal conditioning circuitry 330.

Additional Embodiments

While one embodiment of a physiological monitor incorporating aprocessor of the present invention for determining a reference signalfor use in a waveform scrubber, to remove or derive primary andsecondary components from a physiological measurement has been describedin the form of a pulse oximeter, it will be obvious to one skilled inthe art that other types of physiological monitors may also employ theabove described techniques.

In particular, one skilled in the art will recognize that in all cases,the Fourier transform disclosed above can be replaced by a Fast FourierTransform (FFT), a Chirp-Z Transform, a wavelet transform, a discreteFourier transform, or any other operation that produces the same orsimilar result.

Furthermore, the signal processing techniques described in the presentinvention may be used to compute the arterial and venous blood oxygensaturations of a physiological system on a continuous or nearlycontinuous time basis. These calculations may be performed, regardlessof whether or not the physiological system undergoes voluntary motion.

Furthermore, it will be understood that transformations of measuredsignals other than logarithmic conversion and that the determination ofa proportionality factor which allows removal or derivation of theprimary or secondary signal portions for determination of a referencesignal are possible. Additionally, although the proportionality factor rhas been described herein as a ratio of a portion of a first signal to aportion of a second signal, a similar proportionality constantdetermined as a ratio of a portion of a second signal to a portion of afirst signal could equally well be utilized in the processor of thepresent invention. In the latter case, a secondary reference signalwould generally resemble n′(t)=n_(b)(t)−rn_(a)(t).

One skilled in the art will realize that many different types ofphysiological monitors may employ the teachings of the presentinvention. Other types of physiological monitors include, but are in notlimited to, electro-cardiographs, blood pressure monitors, bloodconstituent monitors (other than oxygen saturation) monitors,capnographs, heart rate monitors, respiration monitors, or depth ofanesthesia monitors. Additionally, monitors which measure the pressureand quantity of a substance within the body such as a breathalyzer, adrug monitor, a cholesterol monitor, a glucose monitor, a carbon dioxidemonitor, a glucose monitor, or a carbon monoxide monitor may also employthe above described techniques.

Furthermore, one skilled in the art will recognize that many of thesignal processing techniques, and many of the filters disclosed hereinare classification techniques. Many of the classification mechanismsherein involve classification of spectral lines and ratios of variousspectral lines. Other classification schemes are possible within thespirit and scope of the invention.

Furthermore, one skilled in the art will realize that the abovedescribed techniques of primary or secondary signal removal orderivation from a composite signal including both primary and secondarycomponents can also be performed on electrocardiography (ECG) signalswhich are derived from positions on the body which are close and highlycorrelated to each other.

Furthermore, one skilled in the art will realize that the abovedescribed techniques can also be performed on signals made up ofreflected energy, rather than transmitted energy. One skilled in the artwill also realize that a primary or secondary portion of a measuredsignal of any type of energy, including but not limited to sound energy,X-ray energy, gamma ray energy, or light energy can be estimated by thetechniques described above. Thus, one skilled in the art will realizethat the techniques of the present invention can be applied in suchmonitors as those using ultrasound where a signal is transmitted througha portion of the body and reflected back from within the body backthrough this portion of the body. Additionally, monitors such asecho-cardiographs may also utilize the techniques of the presentinvention since they too rely on transmission and reflection.

While the present invention has been described in terms of aphysiological monitor, one skilled in the art will realize that thesignal processing techniques of the present invention can be applied inmany areas, including but not limited to the processing of aphysiological signal. The present invention may be applied in anysituation where a signal processor comprising a detector receives afirst signal which includes a first primary signal portion and a firstsecondary signal portion and a second signal which includes a secondprimary signal portion and a second secondary signal portion. Thus, thesignal processor of the present invention is readily applicable tonumerous signal processing areas.

1. A noninvasive physiological monitor comprising: a first input forreceiving an output waveform from a detector responsive light attenuatedby body tissue, the output waveform including at least a first waveformcorresponding to a first wavelength of light attenuated by body tissueand a second waveform corresponding to a second wavelength of lightattenuated by body tissue; a signal processor configured to transformsaid first and second waveforms into spectral domain waveforms, theprocessor further configured to select spectral data based onpredetermined criteria, the signal processor further configured todetermine a physiological indication of the patient based on theselected spectral data.
 2. The noninvasive physiological monitor ofclaim 1, wherein the physiological indication is pulse rate.
 3. Thenoninvasive physiological monitor of claim 1, wherein the criteria islargest spectral peak.
 4. The noninvasive physiological monitor of claim1, wherein the criteria is a peak with corresponding harmonics.
 5. Thenoninvasive physiological monitor of claim 1, wherein the criteria is aspectral peak in a predetermined range.
 6. A method of determiningphysiological information noninvasively based on optical measurements,the method comprising: receiving, from a detector, a waveform indicativeof tissue attenuated light detected by the detector, the waveformincluding at least a first waveform corresponding to a first awavelength of light attenuated by body tissue and a second waveformcorrespond to a second wavelength of light attenuated by body tissue;transforming said first and second waveforms into spectral domainwaveforms, the processor further configured to select spectral databased on predetermined criteria; determining, using one or moreprocessors, a physiological indication of the patient based on theselected spectral data.
 7. The method of claim 6, wherein thephysiological indication is pulse rate.
 8. The method of claim 6,wherein the criteria is largest spectral peak.
 9. The method of claim 6,wherein the criteria is a peak with corresponding harmonics.
 10. Themethod of claim 6, wherein the criteria is a spectral peak in apredetermined range.